# Moving Object Detection Software

Implementation of SOBS algorithm as described in:

L. Maddalena, A. Petrosino, A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications, **IEEE Transactions on Image Processing**, DOI: 10.1109/TIP.2008.924285, Vol. 17, no. 7, pagg. 1168-1177, July 2008

**Usage**:

where

`<SeqName>`

: sequence name (complete path), not including frame numbers. Image sequences consist of binary PPM image frames with consecutive numbers, named in the following form

The number of digits for

`<#FirstFrame>, <#LastFrame>`

: number of first and last sequence frame to be considered.[parameters]: optional, including:

-n #: (square root of) number of weight vectors for each pixel. Default 3

-K #: Number of initial frames for calibration. Default 200

-e1 #: Distance threshold e1 for calibration phase (eqn. (2)). Default 0.1

-e2 #: Distance threshold e2 for online phase (eqn. (2)). Default 0.03

-c1 #: Learning rate c1 for calibration phase (eqn. (4)). Default 1.0

-c2 #: Learning rate c2 for online phase (eqn. (4)). Default 0.05

-g #: Value for g in eqn. (5). Default 0.7

-b #: Value for b in eqn. (5). Default 1.0

-tS #: Value for tS in eqn. (5). Default 0.1

-tH #: Value for tH in eqn. (5). Default 10.0

-s: To apply shadow removal. Default: no shadow removal

-m: To save background model images. Default: do not save

-l: To save just last detection mask. Default: save all

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**Example of use:**

where sequence WavingTrees, coming from sequences adopted in K. Toyama, J. Krumm, B. Brumitt, and B. Meyers, “Wallflower: principles and practice of background maintenance,” in Proc. 7th IEEE Conf. Computer Vision, 1999, vol. 1, pp. 255–261, has been saved in binary PPM image files named:

and stored in directory c:/Sequences/WavingTrees.

Download the SOBS software here. |